Fuzzy Logic-based Analysis of Plant Stress Response using Multiple Environmental Characteristics
Plant tolerance to ecological stresses is the key issue faced by the plant scientists in the agricultural sector. The impact of rapidly changing environment conditions coupled with the scarcity of natural resources has driven an increased adoption in crop adaptive strategies for their controlled growth and development under uncertain ecological conditions. In this paper, we evaluate the plant stress response to environmental factors influencing the growth and productivity of crop species. For this, we employ the multivariate fuzzy logic mechanism for qualitative assessment of different parameters affecting the response of plant stress to multiple climate attributes. These distinct attributes including the soil quality, relative humidity, growth temperature and water availability are simultaneously fed as input variables to the fuzzy inference system. These parameters controlling and determining the plant growth and performance exhibit underlying ambiguities which can be effectively modeled with fuzzy control methodology. For experimental verification of the proposed technique against the conventional method, we employ various error metrics including MAE, RMSE, mean Gamma deviance and mean Poisson deviance. Given the sample record of specific plant species information dataset for the proposed fuzzy framework, simulation results show that the mean error rate of the proposed model evaluated on these metrics is around 25.5%, 29.04%, 15.2% and 11.4%. Finally, the effectiveness of the proposed model is demonstrated by utilizing the Shannon’s entropy and second-order Bossaer’s fairness indices. It is observed that the predicted output has reduced entropy by a factor of 1.054 and improved Bossaer’s fairness by a relative proportion of 1.53.
Keywords: Error metrics, entropy, fairness, fuzzy logic, membership function, plant stress response